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About this paper
Maxim Milakov, Natalia Gimelshein
NVIDIA researchers developed an "Online Softmax" algorithm and a fused Softmax+TopK operation that significantly reduce memory accesses, achieving up to 1.3x speedup for standalone Softmax and up to 5x for Softmax+TopK on GPUs, while maintaining numerical stability.
Key insights
- Softmax performance is often bottlenecked by memory bandwidth on GPUs; minimizing memory accesses is crucial for efficiency.
- The iterative re-normalization of the sum of exponentials allows for a single-pass calculation of the Softmax normalizer (running max and sum) while maintaining numerical stability.
- Fusing Softmax with commonly co-occurring operations like TopK can dramatically improve performance by eliminating redundant memory loads and kernel launch overheads, especially in inference scenarios.
Summary by alphaXiv.
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